Mining quantitative association rules in large relational tables

  • Authors:
  • Ramakrishnan Srikant;Rakesh Agrawal

  • Affiliations:
  • IBM Almaden Research Center, 650 Harry Road, San Jose, CA and Department of Computer Science, University of Wisconsin, Madison;IBM Almaden Research Center, 650 Harry Road, San Jose, CA

  • Venue:
  • SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
  • Year:
  • 1996

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Abstract

We introduce the problem of mining association rules in large relational tables containing both quantitative and categorical attributes. An example of such an association might be "10% of married people between age 50 and 60 have at least 2 cars". We deal with quantitative attributes by fine-partitioning the values of the attribute and then combining adjacent partitions as necessary. We introduce measures of partial completeness which quantify the information lost due to partitioning. A direct application of this technique can generate too many similar rules. We tackle this problem by using a "greater-than-expected-value" interest measure to identify the interesting rules in the output. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.